论文标题
签名网络嵌入，并应用于同时检测社区和异常
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies
论文作者
论文摘要
经常在现实生活中观察到签名的网络，并具有与每个边缘相关的其他标志信息，但是在现有网络模型中，这些信息在很大程度上被忽略了。本文开发了一个统一的嵌入模型，用于签名网络，以消除交织在一起的平衡结构和异常效应，这可以极大地促进下游分析，包括社区检测，异常检测和网络推断。所提出的模型通过低等级加稀疏的基质分解捕获平衡结构和异常效应，这些分解是通过正则配方共同估算的。它的理论保证是根据渐近一致性和用于网络嵌入，社区检测和异常检测的有限样本概率范围的。还通过在合成网络和国际关系网络上进行的广泛数值实验来证明所提出的嵌入模型的优势。
Signed networks are frequently observed in real life with additional sign information associated with each edge, yet such information has been largely ignored in existing network models. This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference. The proposed model captures both balance structure and anomaly effect through a low rank plus sparse matrix decomposition, which are jointly estimated via a regularized formulation. Its theoretical guarantees are established in terms of asymptotic consistency and finite-sample probability bounds for network embedding, community detection and anomaly detection. The advantage of the proposed embedding model is also demonstrated through extensive numerical experiments on both synthetic networks and an international relation network.
